Capacity Factor Research Articles

Predicting individual patient and hospital-level discharge using machine learning

Abstract Background Accurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored. Methods We used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h. We fitted separate extreme gradient boosting models for elective and emergency admissions, trained on the first two years of data and tested on the final year of data. We examined individual-level and hospital-level model performance and evaluated the impact of training data size and recency, prediction time, and performance in subgroups. Results Our models achieve AUROCs of 0.87 and 0.86, AUPRCs of 0.66 and 0.64, and F1 scores of 0.61 and 0.59 for elective and emergency admissions, respectively. These models outperform a logistic regression model using the same features and are substantially better than a baseline logistic regression model with more limited features. Notably, the relative performance increase from adding additional features is greater than the increase from using a sophisticated model. Aggregating individual probabilities, daily total discharge estimates are accurate with mean absolute errors of 8.9% (elective) and 4.9% (emergency). The most informative predictors include antibiotic prescriptions, medications, and hospital capacity factors. Performance remains robust across patient subgroups and different training strategies, but is lower in patients with longer admissions and those who died in hospital. Conclusions Our findings highlight the potential of machine learning in optimising hospital patient flow and facilitating patient care and recovery.

Heterogeneous effects of climate change on displacement-inducing disasters

With an estimated 357.7 million internal displacements caused since 2008, weather-related disasters are a major driver of human mobility worldwide. As climate change is projected to increase the frequency and intensity of extreme weather events in many parts of the world, it is important to better understand how trends in weather patterns related to global warming have affected the intensity of disasters that have caused displacements. Here we combined observational and counterfactual climate data with global internal displacement records to estimate how climate change has affected precipitation and wind speeds at the time and location of floods and storms that led to internal displacements. We estimate that, on average, climate change increased precipitation and decreased wind speeds during such events by +3.7% and − 1.4%, respectively. However, the variability across events is considerable (±28.6 and ± 6.6%, respectively), highlighting the large signal of natural variability of the weather system as compared to the global warming signal. Our results caution against overstating the role of climate change in displacement-inducing disasters in the past, especially compared to socio-economic and development factors of vulnerability and adaptive capacity that determine whether weather-related hazards turn into disasters.’

Improved wind power assessments by bias adjusted reanalysed data with applications near Morocco's coast.

The wind energy requirements for high temporal and spatial observations, unavailable in many countries, are met by reanalysed wind data, which are increasingly used in wind power assessments. However, these data also include unrealistic features, making their validation compulsory. The validation analysis of ERA5 and MERRA-2 wind data, two of the most successful wind reanalyses, with observations near Morocco's coast, highlighted the existence of bias in both datasets, and other shortcomings. The study was conducted at eight selected sites in the period 2011-2020 using linear and probability density function based statistical tests. The local ERA5-BA and MERRA-2 BA velocity fields were created using Bias Adjustment (BA) Techniques. Only part of the observed and modelled datasets (training period) were used to identify the transfer function. The BA fields were verified against observations during the remaining period (verification period). They outperformed their respective non-adjusted fields and also the WFDE5 (the global bias-corrected ERA5 wind field) in the wind potential assessments, represented by capacity factor and low-wind day. At every location, the relative errors in the latter's estimates using ERA5-BA were lowered to less than 10 %, and the same was true for MERRA-2-BA. Thus, an observational dataset of reduced length can help overcome some reanalysed wind data limitations for wind power estimations. Additionally, this methodology can be applied to the correction of each reanalysis update.

The status quo and influencing factors of intrinsic capacity among community-dwelling older adults from the perspective of Ecological Systems Theory: A cross-sectional study.

As intrinsic capacity (IC) declines, older adults are at a significantly increased risk of frailty, care dependency, and death. Currently, the research on IC among older adults in China was still insufficient. We aimed to identify the status quo and influencing factors among community-dwelling older adults in China and explore the relationship between IC, external environment, and social network. A convenience sampling method was used to collect 312 older people from May 2023 to February 2024 in five communities in Chengdu, Sichuan Province. Data were collected using the general information questionnaire, Integrated Care of the Elderly (ICOPE) screening tool, World Health Organization Quality of live scale (WHOQOL-100), and Social Network Scale (LSNS-6). The IC score among Chinese community-dwelling older adults was 3.39 ± 1.60, and the prevalence of IC decline was 86.9%. Marital status, age, number of chronic diseases, social network, and external environment were influencing factors of IC, which explained 35.7% of the total variance. External environment and social network were positively correlated with IC. Chinese community-dwelling older adults had low IC scores and a high prevalence of IC decline. The government should focus on IC for older adults, especially those who are older, not married or widowed, and suffering from multiple chronic diseases. In addition, the richer the external resources available to older adults, the more social support they received, and the better the IC. These findings could provide a theoretical basis for managing and improving IC in older adults.

Novel Fractional Order Differential and Integral Models for Wind Turbine Power–Velocity Characteristics

This work presents an improved modelling approach for wind turbine power curves (WTPCs) using fractional differential equations (FDE). Nine novel FDE-based models are presented for mathematically modelling commercial wind turbine modules’ power–velocity (P-V) characteristics. These models utilize Weibull and Gamma probability density functions to estimate the capacity factor (CF), where accuracy is measured using relative error (RE). Comparative analysis is performed for the WTPC mathematical models with a varying order of differentiation (α) from 0.5 to 1.5, utilizing the manufacturer data for 36 wind turbines with capacities ranging from 150 to 3400 kW. The shortcomings of conventional mathematical models in various meteorological scenarios can be overcome by applying the Riemann–Liouville fractional integral instead of the classical integer-order integrals. By altering the sequence of differentiation and comparing accuracy, the suggested model uses fractional derivatives to increase flexibility. By contrasting the model output with actual data obtained from the wind turbine datasheet and the historical data of a specific location, the models are validated. Their accuracy is assessed using the correlation coefficient (R) and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the exponential model at α=0.9 gives the best accuracy of WTPCs, while the original linear model was the least accurate.

Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines

Abstract. Energy scenarios require realistic estimates of technical wind energy potentials – estimates for how much electricity can be generated by wind turbines given a certain level of deployed capacity within a region. These are typically obtained using observed wind speeds, neglecting the depletion of the wind energy resource with increasing deployment at the regional scale. Here, we use the kinetic energy budget of the atmosphere (KEBA) approach to evaluate the importance of this resource depletion effect for the technical potential of Kansas, USA. To do so, we first apply the KEBA approach to a previously conducted set of numerical simulations with the Weather Research Forecasting (WRF) model. This set simulated the resource depletion effect for a range of different levels of wind turbine deployments within Kansas, which we use to test the KEBA approach. We specifically test the approach for nighttime and daytime conditions to capture the different mixing regimes of the atmospheric boundary layer. We find that KEBA can adequately capture the effect for both settings. We then extend our analysis by using ERA-5 forcing to the climatological scale. We find that this resource depletion effect increases almost linearly with the level of wind turbine deployment. Compared to previously published estimates for the technical potential for Kansas, the resource depletion effect lowers capacity factors by a third to a half. Since this resource depletion effect increases with installed capacity within a region, it is policy relevant even at relatively low deployment levels.

The nexus of industrial employment, financial development, urbanization, and human capital in promoting environmental sustainability in E7 economies

ABSTRACT Financial development supports economic growth, reduces unemployment, ensures justice, and has an impact on the environment. Human development significantly supports E-7 countries in achieving the goal of stable and qualified economic development in the next decade. This factor affects people’s ability to manage environmental problems and increases their productivity by contributing to people’s industrial and production processes. It can be a guide for states to choose technologies that take into account the clean environment and increase energy efficiency in areas where energy is constantly used. Increasing urbanization encourages various development activities. This study aims to investigate the impact of financial development, human development, urbanization, and industrial employment on environmental quality for E-7 countries with regularized common correlated effects and the Augmented Mean Group long-run estimators. Another important aim of the study is to achieve Sustainable Development Goals 4, 8, 9, 11 and 13. Increasing financial development improves environmental quality in Russia and India, according to robust results from long-run estimators. Increasing human development in China increases environmental pollution. While the expansion of urbanization increases environmental quality in Brazil, it decreases it in China. The increase in employment in the industry increases the load capacity factor in the panel group, Brazil, China, Indonesia, and India. In addition to these results, the current article presents policy recommendations for governments to increase environmental quality on a country basis and provides feasible solutions on how to use the independent variables in the study as environmental tools.

How environmental policy stringency, green investment, and renewable energy contribute to environmental pollution and load capacity factor in G11 countries?

AbstractAmid growing concerns about global environmental sustainability, the focus on understanding the interplay between environmental policies, investments in sustainable practices, and renewable energy sources has intensified. This study delves into the relationship between environmental policy stringency, green investment, and the utilization of renewable energy, specifically examining their impact on the load capacity factor (LCF) within G11 countries utilized from 2000 to 2021. Manifestly, LCF integrates both demand‐ and supply‐side aspects of environmental quality. It also utilizes a comprehensive indicator of environmental policy stringency, evaluating its impact alongside green investment. Employing the Cross Sectional‐Augmented Autoregressive Distributed Lag (CS‐ARDL) model and the Common Correlated Effect Mean Group (CCEMG) approach, this research demonstrates that environmental policy stringency, green investment, and renewable energy consumption positively and significantly influence the LCF, consequently fostering improvements in environmental sustainability in both short‐ and long‐term scenarios. It is worth mentioning that environmental policy stringency has the strongest effects on LCF followed by renewable energy. The findings strongly indicate that reinforcing environmental policies, promoting green investment, and embracing renewable energy usage can collectively yield a substantial reduction in environmental pressure for G11 countries.

Extrapolation is not enough: impacts of extreme land use change on wind profiles and wind energy according to regional climate models

Abstract. Humans change climate in many ways. In addition to greenhouse gases, climate models must therefore incorporate a range of other forcings, such as land use change. While studies typically investigate the joint effects of all forcings, here we isolate the impact of afforestation and deforestation on winds in the lowermost 350 m of the atmosphere to assess the relevance of land use change for large-scale wind energy assessments. We use vertically resolved sub-daily output from two regional climate models instead of extrapolating near-surface winds with simplified profiles. Comparing two extreme scenarios, we report that afforestation reduces wind speeds by more than 1 m s−1 in many locations across Europe, even 300 m above ground, underscoring its relevance at hub heights of current and future wind turbines. We show that standard extrapolation with modified parameters approximates long-term means well but fails to capture essential spatio-temporal details, such as changes in the daily cycle, and it is thus insufficient to estimate wind energy potentials. Using adjacent climate model levels to account for spatio-temporal wind profile complexity, we report that wind energy capacity factors are strongly impacted by afforestation and deforestation: they differ by more than 0.1 in absolute terms and up to 50 % in relative terms. Our results confirm earlier studies showing that land use change impacts on wind energy can be severe and that they are generally misrepresented with common extrapolation techniques.

Early-life temperament: A longitudinal investigation of stability and structure

In this longitudinal study, we investigated the factor structure and stability of early-life temperament in a German sample, using three measures developed within Rothbart’s psychobiological approach. Temperament was measured using the Infant Behavior Questionnaire Revised (IBQ-R) at the ages of 6 and 12 months, the Early Childhood Behavior Questionnaire (ECBQ) at the age of 18 months, and the Child Behavior Questionnaire (CBQ) at the ages of 36 and 48 months. We assessed longitudinal stability on a scale and factor level using data from the 85 participants that took part in each measurement. The latent factor structures were analyzed at each time point using all available data, ranging from 112 to 148 participants. Modest to high degrees of stability for most scales and all three latent factors was found. Stability was highest between measurements employing the same instrument and increased with age, indicating a consolidation of temperament traits over time. Model fit for the proposed factor structure consisting of Surgency, Negative Affect, and Effortful Control/Regulatory Capacity was poor, while exploratory factor analyses revealed similar but not identical structures for each time point. The third factor of Regulatory Capacity was not found at 6 months, but at 12 months and later, as is commonly the case in German samples. These findings further contribute to our understanding of the early development of temperament and its stability from infancy and beyond as well as expand on previous literature regarding its structure in German culture.

Material Characteristics of Compressed Dry Masonry Made of Medium-Size Elements with Perlite Aggregate

Dry masonry is a type of construction that is nowadays used to a limited extent in the construction sector, including the housing sector. A lack of codified computational methods enabling engineers to design consciously is one of the factors limiting the development of dry walls. This article presents results from testing an innovative solution for dry masonry made of medium-size elements with expanded perlite aggregate. Material with this type of aggregate has a low bulk density (390 ± 10% kg/m3), which allows the production of large blocks and significantly reduces the value of the thermal conductivity coefficient λ = 0.084 ± 0.003 W/m·K. The results obtained were used to determine material parameters for designing a structure mainly exposed to vertical load. The important practical significance of the presented research results from the lack of provisions, specifications or standards allowing for the design, calculation and construction of dry masonry; it is not possible to analyse the behaviour of this type of structure and to design it consciously and safely. The presented research is therefore an important source of information on mechanical parameters essential for the design of structures and provides tools for this. As a result of the tests of nine panels, the mean compressive strength was determined (1.085 N/mm2), and then the procedure of “design assisted by testing” implemented into Eurocode was used to determine characteristic (0.873 N/mm2) and design compressive strength (0.565 N/mm2). Using the relationships σ-ε, an attempt was made to identify material models for the linear and non-linear analysis of the structure and for designing cross-sections. The material models were made considering increased non-linear deformations of a structure under low stresses (the compression toe) which are true and typical for dry masonry. A specific deformability of dry masonry, slightly different to that in masonry structures joined together with mortar, also affects the reduction factor for load-bearing capacity due to second-order effects. Reduction factors determined from true non-linear deformations were lower than the values specified by EC6 for masonry structures.

Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability.

Performance analysis of a photovoltaic component integrated into a hybrid power plant in Southeast Mauritania

This study investigated the performance of photovoltaic components of the 1.3MW KIFFA hybrid power plant in Mauritania. Data from the plant's monitoring system (January-December 2021) was used to assess various performance metrics. The analysis revealed a high daily reference yield (5.60 h/d), indicating good solar resource availability. However, final and array yields (4.78 h/d and 4.86 h/d, respectively) suggested potential for improvement. System component efficiencies were within acceptable ranges, with particularly high inverter efficiency (98.31%). Array capture losses were moderate (0.74 h/d), and system losses were minimal (0.08 h/d). The annual performance ratio (86.33%) and capacity factor (19.91%) indicated good overall plant performance. These findings were then compared with data from similar installations in various climate zones to understand the impact of climatic variations on photovoltaic performance. Compared to installations in temperate zones with lower irradiation levels, the KIFFA plant's reference yield was significantly higher. However, the final and array yields were closer due to potentially higher operating temperatures in Mauritania affecting module efficiency. Interestingly, comparisons with installations in other desert regions with similarly high irradiation levels revealed lower performance, particularly in terms of final yield, (4.71 h/d) in Algéria (Adrar) and (4.10 h/d) in Oman (Muscat). This suggests that climatic factors beyond just sunlight availability, such as dust accumulation, may have played a significant role in their performance compared to the KIFFA plant.

Green hydrogen as a sustainable operations strategy: a socio-economic perspective

Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use is manifested. This work aims to propose a socio-economic analysis of hydrogen production from an integrated wind and electrolysis plant in southern Italy. The estimated production amounts to about 1.8 million kg and the LCOH is calculated to be 3.60 €/kg in the base scenario. Analyses of the alternative scenarios allow us to observe that with a high probability the value ranges between 3.20-4.00 €/kg and that the capacity factor is the factor that most affects the economic results. Social analysis, conducted through an online survey, shows a strong knowledge gap as only 27.5% claim to know the difference between green and grey hydrogen. There is a slight propensity to install systems near their homes, but this tends to increase due to increased knowledge on the topic. Respondents state sustainable behaviours, and this study suggests that these aspects should also be transformed into the energy choices that are implemented every day. The study suggests information to policy-makers, businesses and citizens as it outlines that green hydrogen is an operations strategy that moves toward sustainable development.

Advancing global solar photovoltaic power forecasting with sub-seasonal climate outlooks

Given the high weather dependency of solar photovoltaic energy, accurate weather information ranging from days to weeks in advance are required for stable plant operation and management. This study emphasizes that sub-seasonal climate outlooks can advance global solar power forecasting. We evaluate the capacity factor (CF) for standard test conditions (CFstc), calculated from atmospheric reanalysis of surface solar irradiance and ambient air temperature, by comparing it with actual CF from two solar plants in Korea. Results confirm CFstc as a reliable estimate of actual CF. Application of this methodology to four-week outlooks from two state-of-the-art sub-seasonal climate forecast systems shows a significant anomaly correlation coefficient (ACC) skill for global solar power forecasting at least one week in advance (5–11 forecast days). Regional ACC skills vary at extended lead times, with South Asia, eastern South America and eastern Australia maintaining ACC > 0.6 for up to two weeks (12–18 forecast days). Notably, useful ACC skill persists for up to four weeks (26–32 forecast days) across 70.9 % of global land areas. These findings suggest that sub-seasonal climate outlooks can provide decision-making information in developing more reliable solar energy strategies, highlighting the importance of transdisciplinary collaboration between renewable energy and meteorological sectors.

Equivalent thickness method of CFRP in chloride ion diffusion equation for CFRP–strengthened RC members and its load capacity assessment method

Carbon fiber reinforced polymer (CFRP)–strengthened reinforced concrete (RC) structures exhibit resistance to chloride ion penetration. However, accurately quantifying this resistance and predicting the load capacity of CFRP–strengthened RC beams subjected to chloride ion erosion requires further investigation. This paper introduces two equivalent thickness methods for non-fully bonded CFRP within the chloride ion diffusion equation: first method assumes CFRP having a fixed equivalent thickness, while the second method allows for CFRP having a linearly decreasing equivalent thickness depending on the depth within the concrete. A model is established between the reduction factor of load capacity in CFRP–strengthened RC beams and the concentration of diffusing chloride ions. Based on experimental results from our research group regarding chloride ion erosion in CFRP–strengthened RC members, both the equivalent thickness method and the load capacity model for CFRP were evaluated. The findings indicate that the chloride ion concentration predicted by the second equivalent thickness method outperforms the first, with an error of 13.1%. Furthermore, this method accurately predicts the chloride ion concentration after 60 days of exposure, exhibiting an error of 7.0%. Additionally, employing the proposed load capacity model, derived from the second equivalent thickness method, the calculated load capacity of CFRP–strengthened RC members subjected to chloride ion erosion shows only a 5.2% deviation from experimental results. This study concludes that the equivalent thickness of non-fully bonded CFRP decreases linearly with depth in concrete and establishes a direct correlation between load capacity and chloride ion concentration, thereby offering more targeted characterization methods.

The contribution of green technological innovation, clean energy, and oil rents in improving the load capacity factor and achieving SDG13 in Saudi Arabia

This research aims to assess the effects of green technological innovation, renewable energy sources, and oil rents on the load capacity factor in Saudi Arabia from 1988 to 2021. The primary conclusions can be outlined as follows. The combined cointegration and Saikkonen-Lütkepohl cointegration tests reveal long-run relationships between the load capacity factor and the explanatory variables at the 1% significance level. In comparison, the Phillips-Ouliaris test shows evidence of cointegration only at 10%. Moreover, the quantile regression indicates that oil rents adversely impact environmental quality; however, they remain contingent upon environmental conditions. A 1% increase in oil rents results in a decline in environmental quality by 0.025% under poor conditions, 0.036% under moderate/normal conditions, and 0.108% under good conditions. On the contrary, renewable energy consumption and green technological innovation improve environmental quality, irrespective of the prevailing environmental conditions. However, the environmental impacts of renewable energy consumption exceed those of green technological innovation. Results show that a 1% increase in renewable energy consumption leads to a 0.052-0.253% improvement in environmental quality, whereas a 1% increase in green technological innovation results only in a 0.017-0.047% improvement. Finally, population and GDP per capita exert negative and positive implications on the load capacity factor, respectively, while energy intensity has no significant environmental effects. The research findings provide significant insights and suggest policy recommendations to address climate change and meet the targets set out in SDG13.

The role of financial inclusion and technological innovation in stimulating environmental sustainability in the European countries: A new perspective based on load capacity factor

Given the alarming level of climate change, policymakers across the globe are seeking strategies to mitigate environmental pollution to achieve sustainable development. In this context, renewable energy and technological advancements have emerged as an effective way to lower pollution and attain sustainable development. This study evaluates the effect of financial inclusion, technological innovation, and renewable energy on the load capacity factor (LCF) in European countries from 2004 to 2018. LCF is considered the most comprehensive indicator of ecological sustainability, combining both the biocapacity factor and ecological footprint. Hence, the present work fills the literature gap by exploring, for the first time, the effect of financial inclusion on the LCF. Applying the advanced Method of Moment Quantile Regression (MMQR), the study demonstrates that technological innovation and economic growth have adverse effects on LCF while renewable energy and financial inclusion promote LCF. The study indicates that technological innovation and economic growth undermine ecological excellence in European nations while green energy and financial inclusion enhance it. Moreover, the findings of the causality analysis reveal a causal association between financial inclusion, renewable energy, and LCF. Our study recommends prioritizing financial inclusion alongside investments in renewable energy to enhance ecological sustainability.

Load Capacity Factor and Environmental Quality: Unveiling the Role of Economic Growth, Green Innovations, and Environmental Policies in G20 Economies

Load Capacity Factor and Environmental Quality: Unveiling the Role of Economic Growth, Green Innovations, and Environmental Policies in G20 Economies

Importance of Nuclear Energy to Accomplish Net Zero Carbon

The world is entangled with the urgent need for existing energy modes transition to clean energy for mitigating global warming and climate change. It ultimately demands to set stringent goals for achieving net zero carbon for each country. In this direction different approaches are being stipulated and one of the important options emerges as nuclear power, a compelling option for sustainable development. Unlike renewable energy sources such as solar, wind and nuclear energy which provides a continuous and reliable supply of electricity with minimal greenhouse gas emissions. On the other hand nuclear power plants operate with high capacity factor compared to variable renewable sources that depend on weather conditions and its reliability makes nuclear energy a strong candidate for baseload power. Moreover, nuclear energy's low carbon footprint and high energy density output makes it an attractive alternative to fossil fuels. Although, it is crucial to acknowledge the challenges associated with nuclear energy, including the safe management of nuclear waste, the risk of nuclear accidents, and the high upfront costs of building nuclear power plants. Hence, a balanced perspective on the future of nuclear energy and its potential to contribute to a sustainable future is to be adopted carefully. However, a periodic advancements in technology and stringent safety measures, modern Nuclear reactors are designed to minimize environmental impact and enhance safety.